Files
Claude-Code-Workflow/.codex/skills/team-issue/roles/explorer/role.md
catlog22 1e560ab8e8 feat: migrate all codex team skills from spawn_agents_on_csv to spawn_agent + wait_agent architecture
- Delete 21 old team skill directories using CSV-wave pipeline pattern (~100+ files)
- Delete old team-lifecycle (v3) and team-planex-v2
- Create generic team-worker.toml and team-supervisor.toml (replacing tlv4-specific TOMLs)
- Convert 19 team skills from Claude Code format (Agent/SendMessage/TaskCreate)
  to Codex format (spawn_agent/wait_agent/tasks.json/request_user_input)
- Update team-lifecycle-v4 to use generic agent types (team_worker/team_supervisor)
- Convert all coordinator role files: dispatch.md, monitor.md, role.md
- Convert all worker role files: remove run_in_background, fix Bash syntax
- Convert all specs/pipelines.md references
- Final state: 20 team skills, 217 .md files, zero Claude Code API residuals

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-24 16:54:48 +08:00

3.5 KiB

role, prefix, inner_loop, message_types
role prefix inner_loop message_types
explorer EXPLORE false
context_ready
error

Issue Explorer

Analyze issue context, explore codebase for relevant files, map dependencies and impact scope. Produce a shared context report for planner, reviewer, and implementer.

Phase 2: Issue Loading & Context Setup

Input Source Required
Issue ID Task description (GH-\d+ or ISS-\d{8}-\d{6}) Yes
Issue details ccw issue status <id> --json Yes
Session path Extracted from task description Yes
wisdom meta /wisdom/.msg/meta.json No
  1. Extract issue ID from task description via regex: (?:GH-\d+|ISS-\d{8}-\d{6})
  2. If no issue ID found -> report error, STOP
  3. Load issue details:
Bash("ccw issue status <issueId> --json")
  1. Parse JSON response for issue metadata (title, context, priority, labels, feedback)
  2. Load wisdom files from <session>/wisdom/ if available

Phase 3: Codebase Exploration & Impact Analysis

Complexity assessment determines exploration depth:

Signal Weight Keywords
Structural change +2 refactor, architect, restructure, module, system
Cross-cutting +2 multiple, across, cross
Integration +1 integrate, api, database
High priority +1 priority >= 4
Score Complexity Strategy
>= 4 High Deep exploration via CLI tool
2-3 Medium Hybrid: ACE search + selective CLI
0-1 Low Direct ACE search only

Exploration execution:

Complexity Execution
Low Direct ACE search: mcp__ace-tool__search_context(project_root_path, query)
Medium/High CLI exploration: Bash("ccw cli -p \"<exploration_prompt>\" --tool gemini --mode analysis")

CLI exploration prompt template:

PURPOSE: Explore codebase for issue <issueId> to identify relevant files, dependencies, and impact scope; success = comprehensive context report written to <session>/explorations/context-<issueId>.json

TASK: * Run ccw tool exec get_modules_by_depth '{}' * Execute ACE searches for issue keywords * Map file dependencies and integration points * Assess impact scope * Find existing patterns * Check git log for related changes

MODE: analysis

CONTEXT: @**/* | Memory: Issue <issueId> - <issue.title> (Priority: <issue.priority>)

EXPECTED: JSON report with: relevant_files (path + relevance), dependencies, impact_scope (low/medium/high), existing_patterns, related_changes, key_findings, complexity_assessment

CONSTRAINTS: Focus on issue context | Write output to <session>/explorations/context-<issueId>.json

Report schema:

{
  "issue_id": "string",
  "issue": { "id": "", "title": "", "priority": 0, "status": "", "labels": [], "feedback": "" },
  "relevant_files": [{ "path": "", "relevance": "" }],
  "dependencies": [],
  "impact_scope": "low | medium | high",
  "existing_patterns": [],
  "related_changes": [],
  "key_findings": [],
  "complexity_assessment": "Low | Medium | High"
}

Phase 4: Context Report & Wisdom Contribution

  1. Write context report to <session>/explorations/context-<issueId>.json
  2. If file not found from agent, build minimal report from ACE results
  3. Update <session>/wisdom/.msg/meta.json under explorer namespace:
    • Read existing -> merge { "explorer": { issue_id, complexity, impact_scope, file_count } } -> write back
  4. Contribute discoveries to <session>/wisdom/learnings.md if new patterns found